Overview

Dataset statistics

Number of variables29
Number of observations25272
Missing cells0
Missing cells (%)0.0%
Duplicate rows1542
Duplicate rows (%)6.1%
Total size in memory5.6 MiB
Average record size in memory232.0 B

Variable types

Numeric19
Categorical10

Alerts

Dataset has 1542 (6.1%) duplicate rowsDuplicates
Семестр is highly overall correlated with Накоп зачет сразу and 1 other fieldsHigh correlation
зачет сразу is highly overall correlated with незачет сразуHigh correlation
удовлетворительно сразу is highly overall correlated with Накоп удовлетворительно сразуHigh correlation
отлично сразу is highly overall correlated with Накоп отлично сразуHigh correlation
зачет с исправлением is highly overall correlated with незачет до исправленияHigh correlation
незачет до исправления is highly overall correlated with зачет с исправлением and 1 other fieldsHigh correlation
Накоп незачет сразу is highly overall correlated with незачет сразуHigh correlation
Накоп зачет сразу is highly overall correlated with Семестр and 3 other fieldsHigh correlation
Накоп удовлетворительно сразу is highly overall correlated with удовлетворительно сразу and 1 other fieldsHigh correlation
Накоп хорошо сразу is highly overall correlated with Семестр and 1 other fieldsHigh correlation
Накоп отлично сразу is highly overall correlated with отлично сразу and 1 other fieldsHigh correlation
Накоп зачет с исправлением is highly overall correlated with Накоп незачет до исправленияHigh correlation
Накоп удовлетворительно с исправлением is highly overall correlated with Накоп зачет до исправленияHigh correlation
Накоп хорошо с исправлением is highly overall correlated with Накоп удовлетворительно до исправленияHigh correlation
Накоп незачет до исправления is highly overall correlated with незачет до исправления and 1 other fieldsHigh correlation
Накоп зачет до исправления is highly overall correlated with Накоп удовлетворительно с исправлениемHigh correlation
Накоп удовлетворительно до исправления is highly overall correlated with Накоп хорошо с исправлениемHigh correlation
незачет сразу is highly overall correlated with зачет сразу and 2 other fieldsHigh correlation
хорошо с исправлением is highly overall correlated with удовлетворительно до исправленияHigh correlation
отлично с исправлением is highly overall correlated with хорошо до исправленияHigh correlation
удовлетворительно до исправления is highly overall correlated with хорошо с исправлениемHigh correlation
хорошо до исправления is highly overall correlated with отлично с исправлениемHigh correlation
отчислен is highly overall correlated with незачет сразуHigh correlation
удовлетворительно с исправлением is highly imbalanced (90.7%)Imbalance
хорошо с исправлением is highly imbalanced (80.7%)Imbalance
отлично с исправлением is highly imbalanced (75.0%)Imbalance
зачет до исправления is highly imbalanced (75.8%)Imbalance
удовлетворительно до исправления is highly imbalanced (83.4%)Imbalance
хорошо до исправления is highly imbalanced (82.4%)Imbalance
зачет с исправлением is highly skewed (γ1 = 23.73606165)Skewed
незачет до исправления is highly skewed (γ1 = 20.2705086)Skewed
Накоп зачет с исправлением is highly skewed (γ1 = 20.1790502)Skewed
зачет сразу has 2260 (8.9%) zerosZeros
удовлетворительно сразу has 15461 (61.2%) zerosZeros
хорошо сразу has 10299 (40.8%) zerosZeros
отлично сразу has 13657 (54.0%) zerosZeros
зачет с исправлением has 25139 (99.5%) zerosZeros
незачет до исправления has 24880 (98.4%) zerosZeros
Накоп незачет сразу has 19155 (75.8%) zerosZeros
Накоп зачет сразу has 663 (2.6%) zerosZeros
Накоп удовлетворительно сразу has 9511 (37.6%) zerosZeros
Накоп хорошо сразу has 4179 (16.5%) zerosZeros
Накоп отлично сразу has 7277 (28.8%) zerosZeros
Накоп зачет с исправлением has 24687 (97.7%) zerosZeros
Накоп удовлетворительно с исправлением has 22394 (88.6%) zerosZeros
Накоп хорошо с исправлением has 19644 (77.7%) zerosZeros
Накоп отлично с исправлением has 18696 (74.0%) zerosZeros
Накоп незачет до исправления has 23903 (94.6%) zerosZeros
Накоп зачет до исправления has 18909 (74.8%) zerosZeros
Накоп удовлетворительно до исправления has 21576 (85.4%) zerosZeros

Reproduction

Analysis started2023-10-04 03:59:17.778177
Analysis finished2023-10-04 04:00:14.052837
Duration56.27 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Семестр
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.0286879
Minimum0
Maximum12
Zeros21
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size197.6 KiB
2023-10-04T11:00:14.131412image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q36
95-th percentile9
Maximum12
Range12
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.5305556
Coefficient of variation (CV)0.62813395
Kurtosis-0.72493245
Mean4.0286879
Median Absolute Deviation (MAD)2
Skewness0.57676378
Sum101813
Variance6.4037118
MonotonicityNot monotonic
2023-10-04T11:00:14.295128image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
1 4641
18.4%
2 4302
17.0%
3 3961
15.7%
4 2693
10.7%
5 2295
9.1%
6 2201
8.7%
7 1951
7.7%
8 1918
7.6%
9 694
 
2.7%
10 562
 
2.2%
Other values (3) 54
 
0.2%
ValueCountFrequency (%)
0 21
 
0.1%
1 4641
18.4%
2 4302
17.0%
3 3961
15.7%
4 2693
10.7%
5 2295
9.1%
6 2201
8.7%
7 1951
7.7%
8 1918
7.6%
9 694
 
2.7%
ValueCountFrequency (%)
12 2
 
< 0.1%
11 31
 
0.1%
10 562
 
2.2%
9 694
 
2.7%
8 1918
7.6%
7 1951
7.7%
6 2201
8.7%
5 2295
9.1%
4 2693
10.7%
3 3961
15.7%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size197.6 KiB
0
16312 
2
8825 
1
 
135

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters25272
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
0 16312
64.5%
2 8825
34.9%
1 135
 
0.5%

Length

2023-10-04T11:00:14.466994image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-04T11:00:14.675610image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0 16312
64.5%
2 8825
34.9%
1 135
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 16312
64.5%
2 8825
34.9%
1 135
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 25272
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16312
64.5%
2 8825
34.9%
1 135
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Common 25272
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16312
64.5%
2 8825
34.9%
1 135
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 25272
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16312
64.5%
2 8825
34.9%
1 135
 
0.5%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size197.6 KiB
0
18768 
2
3862 
1
 
1405
3
 
1237

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters25272
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 18768
74.3%
2 3862
 
15.3%
1 1405
 
5.6%
3 1237
 
4.9%

Length

2023-10-04T11:00:14.867476image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-04T11:00:15.062706image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0 18768
74.3%
2 3862
 
15.3%
1 1405
 
5.6%
3 1237
 
4.9%

Most occurring characters

ValueCountFrequency (%)
0 18768
74.3%
2 3862
 
15.3%
1 1405
 
5.6%
3 1237
 
4.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 25272
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 18768
74.3%
2 3862
 
15.3%
1 1405
 
5.6%
3 1237
 
4.9%

Most occurring scripts

ValueCountFrequency (%)
Common 25272
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 18768
74.3%
2 3862
 
15.3%
1 1405
 
5.6%
3 1237
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 25272
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 18768
74.3%
2 3862
 
15.3%
1 1405
 
5.6%
3 1237
 
4.9%

незачет сразу
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size197.6 KiB
0
21025 
1
4247 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters25272
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 21025
83.2%
1 4247
 
16.8%

Length

2023-10-04T11:00:15.179059image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-04T11:00:15.306929image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0 21025
83.2%
1 4247
 
16.8%

Most occurring characters

ValueCountFrequency (%)
0 21025
83.2%
1 4247
 
16.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 25272
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 21025
83.2%
1 4247
 
16.8%

Most occurring scripts

ValueCountFrequency (%)
Common 25272
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 21025
83.2%
1 4247
 
16.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 25272
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 21025
83.2%
1 4247
 
16.8%

зачет сразу
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.532447
Minimum0
Maximum11
Zeros2260
Zeros (%)8.9%
Negative0
Negative (%)0.0%
Memory size197.6 KiB
2023-10-04T11:00:15.410820image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q35
95-th percentile6
Maximum11
Range11
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.8860477
Coefficient of variation (CV)0.53392102
Kurtosis-0.28284189
Mean3.532447
Median Absolute Deviation (MAD)1
Skewness-0.15249925
Sum89272
Variance3.557176
MonotonicityNot monotonic
2023-10-04T11:00:15.530628image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
4 6379
25.2%
5 4634
18.3%
3 3568
14.1%
2 3079
12.2%
6 2588
10.2%
0 2260
 
8.9%
1 1930
 
7.6%
7 584
 
2.3%
8 142
 
0.6%
10 50
 
0.2%
Other values (2) 58
 
0.2%
ValueCountFrequency (%)
0 2260
 
8.9%
1 1930
 
7.6%
2 3079
12.2%
3 3568
14.1%
4 6379
25.2%
5 4634
18.3%
6 2588
10.2%
7 584
 
2.3%
8 142
 
0.6%
9 48
 
0.2%
ValueCountFrequency (%)
11 10
 
< 0.1%
10 50
 
0.2%
9 48
 
0.2%
8 142
 
0.6%
7 584
 
2.3%
6 2588
10.2%
5 4634
18.3%
4 6379
25.2%
3 3568
14.1%
2 3079
12.2%

удовлетворительно сразу
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.73290598
Minimum0
Maximum9
Zeros15461
Zeros (%)61.2%
Negative0
Negative (%)0.0%
Memory size197.6 KiB
2023-10-04T11:00:15.650523image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum9
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1112954
Coefficient of variation (CV)1.5162864
Kurtosis1.7986153
Mean0.73290598
Median Absolute Deviation (MAD)0
Skewness1.5355002
Sum18522
Variance1.2349775
MonotonicityNot monotonic
2023-10-04T11:00:15.780697image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 15461
61.2%
1 4426
 
17.5%
2 2969
 
11.7%
3 1651
 
6.5%
4 649
 
2.6%
5 93
 
0.4%
6 20
 
0.1%
8 1
 
< 0.1%
9 1
 
< 0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
0 15461
61.2%
1 4426
 
17.5%
2 2969
 
11.7%
3 1651
 
6.5%
4 649
 
2.6%
5 93
 
0.4%
6 20
 
0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
9 1
 
< 0.1%
8 1
 
< 0.1%
7 1
 
< 0.1%
6 20
 
0.1%
5 93
 
0.4%
4 649
 
2.6%
3 1651
 
6.5%
2 2969
 
11.7%
1 4426
 
17.5%
0 15461
61.2%

хорошо сразу
Real number (ℝ)

ZEROS 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1085787
Minimum0
Maximum11
Zeros10299
Zeros (%)40.8%
Negative0
Negative (%)0.0%
Memory size197.6 KiB
2023-10-04T11:00:15.902637image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile3
Maximum11
Range11
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.1894312
Coefficient of variation (CV)1.0729335
Kurtosis1.2580117
Mean1.1085787
Median Absolute Deviation (MAD)1
Skewness1.0307768
Sum28016
Variance1.4147466
MonotonicityNot monotonic
2023-10-04T11:00:16.016447image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 10299
40.8%
1 6653
26.3%
2 4812
19.0%
3 2511
 
9.9%
4 871
 
3.4%
5 85
 
0.3%
6 21
 
0.1%
9 6
 
< 0.1%
8 5
 
< 0.1%
7 5
 
< 0.1%
Other values (2) 4
 
< 0.1%
ValueCountFrequency (%)
0 10299
40.8%
1 6653
26.3%
2 4812
19.0%
3 2511
 
9.9%
4 871
 
3.4%
5 85
 
0.3%
6 21
 
0.1%
7 5
 
< 0.1%
8 5
 
< 0.1%
9 6
 
< 0.1%
ValueCountFrequency (%)
11 2
 
< 0.1%
10 2
 
< 0.1%
9 6
 
< 0.1%
8 5
 
< 0.1%
7 5
 
< 0.1%
6 21
 
0.1%
5 85
 
0.3%
4 871
 
3.4%
3 2511
9.9%
2 4812
19.0%

отлично сразу
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct14
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.94064577
Minimum0
Maximum13
Zeros13657
Zeros (%)54.0%
Negative0
Negative (%)0.0%
Memory size197.6 KiB
2023-10-04T11:00:16.136599image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile4
Maximum13
Range13
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.2817449
Coefficient of variation (CV)1.3626223
Kurtosis2.8587804
Mean0.94064577
Median Absolute Deviation (MAD)0
Skewness1.466566
Sum23772
Variance1.64287
MonotonicityNot monotonic
2023-10-04T11:00:16.280648image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 13657
54.0%
1 4921
 
19.5%
2 2970
 
11.8%
3 2292
 
9.1%
4 1263
 
5.0%
5 119
 
0.5%
6 23
 
0.1%
7 8
 
< 0.1%
8 5
 
< 0.1%
9 4
 
< 0.1%
Other values (4) 10
 
< 0.1%
ValueCountFrequency (%)
0 13657
54.0%
1 4921
 
19.5%
2 2970
 
11.8%
3 2292
 
9.1%
4 1263
 
5.0%
5 119
 
0.5%
6 23
 
0.1%
7 8
 
< 0.1%
8 5
 
< 0.1%
9 4
 
< 0.1%
ValueCountFrequency (%)
13 3
 
< 0.1%
12 3
 
< 0.1%
11 3
 
< 0.1%
10 1
 
< 0.1%
9 4
 
< 0.1%
8 5
 
< 0.1%
7 8
 
< 0.1%
6 23
 
0.1%
5 119
 
0.5%
4 1263
5.0%

зачет с исправлением
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0075973409
Minimum0
Maximum5
Zeros25139
Zeros (%)99.5%
Negative0
Negative (%)0.0%
Memory size197.6 KiB
2023-10-04T11:00:16.432507image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.12526594
Coefficient of variation (CV)16.488129
Kurtosis703.33701
Mean0.0075973409
Median Absolute Deviation (MAD)0
Skewness23.736062
Sum192
Variance0.015691556
MonotonicityNot monotonic
2023-10-04T11:00:16.628548image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 25139
99.5%
1 102
 
0.4%
2 16
 
0.1%
4 7
 
< 0.1%
3 5
 
< 0.1%
5 3
 
< 0.1%
ValueCountFrequency (%)
0 25139
99.5%
1 102
 
0.4%
2 16
 
0.1%
3 5
 
< 0.1%
4 7
 
< 0.1%
5 3
 
< 0.1%
ValueCountFrequency (%)
5 3
 
< 0.1%
4 7
 
< 0.1%
3 5
 
< 0.1%
2 16
 
0.1%
1 102
 
0.4%
0 25139
99.5%
Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size197.6 KiB
0.0
24460 
1.0
 
762
2.0
 
45
3.0
 
4
5.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters75816
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 24460
96.8%
1.0 762
 
3.0%
2.0 45
 
0.2%
3.0 4
 
< 0.1%
5.0 1
 
< 0.1%

Length

2023-10-04T11:00:16.824750image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-04T11:00:16.980912image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 24460
96.8%
1.0 762
 
3.0%
2.0 45
 
0.2%
3.0 4
 
< 0.1%
5.0 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 49732
65.6%
. 25272
33.3%
1 762
 
1.0%
2 45
 
0.1%
3 4
 
< 0.1%
5 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 50544
66.7%
Other Punctuation 25272
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 49732
98.4%
1 762
 
1.5%
2 45
 
0.1%
3 4
 
< 0.1%
5 1
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 25272
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 75816
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 49732
65.6%
. 25272
33.3%
1 762
 
1.0%
2 45
 
0.1%
3 4
 
< 0.1%
5 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 75816
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 49732
65.6%
. 25272
33.3%
1 762
 
1.0%
2 45
 
0.1%
3 4
 
< 0.1%
5 1
 
< 0.1%

хорошо с исправлением
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size197.6 KiB
0.0
23145 
1.0
 
1983
2.0
 
139
3.0
 
3
4.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters75816
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 23145
91.6%
1.0 1983
 
7.8%
2.0 139
 
0.6%
3.0 3
 
< 0.1%
4.0 2
 
< 0.1%

Length

2023-10-04T11:00:17.193269image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-04T11:00:17.497305image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 23145
91.6%
1.0 1983
 
7.8%
2.0 139
 
0.6%
3.0 3
 
< 0.1%
4.0 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 48417
63.9%
. 25272
33.3%
1 1983
 
2.6%
2 139
 
0.2%
3 3
 
< 0.1%
4 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 50544
66.7%
Other Punctuation 25272
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 48417
95.8%
1 1983
 
3.9%
2 139
 
0.3%
3 3
 
< 0.1%
4 2
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 25272
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 75816
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 48417
63.9%
. 25272
33.3%
1 1983
 
2.6%
2 139
 
0.2%
3 3
 
< 0.1%
4 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 75816
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 48417
63.9%
. 25272
33.3%
1 1983
 
2.6%
2 139
 
0.2%
3 3
 
< 0.1%
4 2
 
< 0.1%

отлично с исправлением
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size197.6 KiB
0.0
22348 
1.0
2583 
2.0
 
325
3.0
 
14
4.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters75816
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 22348
88.4%
1.0 2583
 
10.2%
2.0 325
 
1.3%
3.0 14
 
0.1%
4.0 2
 
< 0.1%

Length

2023-10-04T11:00:17.685511image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-04T11:00:18.105414image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 22348
88.4%
1.0 2583
 
10.2%
2.0 325
 
1.3%
3.0 14
 
0.1%
4.0 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 47620
62.8%
. 25272
33.3%
1 2583
 
3.4%
2 325
 
0.4%
3 14
 
< 0.1%
4 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 50544
66.7%
Other Punctuation 25272
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 47620
94.2%
1 2583
 
5.1%
2 325
 
0.6%
3 14
 
< 0.1%
4 2
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 25272
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 75816
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 47620
62.8%
. 25272
33.3%
1 2583
 
3.4%
2 325
 
0.4%
3 14
 
< 0.1%
4 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 75816
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 47620
62.8%
. 25272
33.3%
1 2583
 
3.4%
2 325
 
0.4%
3 14
 
< 0.1%
4 2
 
< 0.1%

незачет до исправления
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.021881925
Minimum0
Maximum8
Zeros24880
Zeros (%)98.4%
Negative0
Negative (%)0.0%
Memory size197.6 KiB
2023-10-04T11:00:18.286212image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum8
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.23223126
Coefficient of variation (CV)10.612926
Kurtosis563.22744
Mean0.021881925
Median Absolute Deviation (MAD)0
Skewness20.270509
Sum553
Variance0.053931356
MonotonicityNot monotonic
2023-10-04T11:00:18.405906image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 24880
98.4%
1 324
 
1.3%
2 39
 
0.2%
4 8
 
< 0.1%
3 7
 
< 0.1%
8 7
 
< 0.1%
6 3
 
< 0.1%
5 2
 
< 0.1%
7 2
 
< 0.1%
ValueCountFrequency (%)
0 24880
98.4%
1 324
 
1.3%
2 39
 
0.2%
3 7
 
< 0.1%
4 8
 
< 0.1%
5 2
 
< 0.1%
6 3
 
< 0.1%
7 2
 
< 0.1%
8 7
 
< 0.1%
ValueCountFrequency (%)
8 7
 
< 0.1%
7 2
 
< 0.1%
6 3
 
< 0.1%
5 2
 
< 0.1%
4 8
 
< 0.1%
3 7
 
< 0.1%
2 39
 
0.2%
1 324
 
1.3%
0 24880
98.4%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size197.6 KiB
0.0
22968 
1.0
 
2073
2.0
 
229
3.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters75816
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 22968
90.9%
1.0 2073
 
8.2%
2.0 229
 
0.9%
3.0 2
 
< 0.1%

Length

2023-10-04T11:00:18.550086image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-04T11:00:18.686023image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 22968
90.9%
1.0 2073
 
8.2%
2.0 229
 
0.9%
3.0 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 48240
63.6%
. 25272
33.3%
1 2073
 
2.7%
2 229
 
0.3%
3 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 50544
66.7%
Other Punctuation 25272
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 48240
95.4%
1 2073
 
4.1%
2 229
 
0.5%
3 2
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 25272
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 75816
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 48240
63.6%
. 25272
33.3%
1 2073
 
2.7%
2 229
 
0.3%
3 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 75816
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 48240
63.6%
. 25272
33.3%
1 2073
 
2.7%
2 229
 
0.3%
3 2
 
< 0.1%

удовлетворительно до исправления
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size197.6 KiB
0.0
23854 
1.0
 
1319
2.0
 
98
3.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters75816
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 23854
94.4%
1.0 1319
 
5.2%
2.0 98
 
0.4%
3.0 1
 
< 0.1%

Length

2023-10-04T11:00:18.802946image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-04T11:00:18.941981image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 23854
94.4%
1.0 1319
 
5.2%
2.0 98
 
0.4%
3.0 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 49126
64.8%
. 25272
33.3%
1 1319
 
1.7%
2 98
 
0.1%
3 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 50544
66.7%
Other Punctuation 25272
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 49126
97.2%
1 1319
 
2.6%
2 98
 
0.2%
3 1
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 25272
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 75816
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 49126
64.8%
. 25272
33.3%
1 1319
 
1.7%
2 98
 
0.1%
3 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 75816
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 49126
64.8%
. 25272
33.3%
1 1319
 
1.7%
2 98
 
0.1%
3 1
 
< 0.1%

хорошо до исправления
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size197.6 KiB
0.0
23438 
1.0
 
1663
2.0
 
164
3.0
 
5
4.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters75816
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 23438
92.7%
1.0 1663
 
6.6%
2.0 164
 
0.6%
3.0 5
 
< 0.1%
4.0 2
 
< 0.1%

Length

2023-10-04T11:00:19.122390image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-04T11:00:19.298418image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 23438
92.7%
1.0 1663
 
6.6%
2.0 164
 
0.6%
3.0 5
 
< 0.1%
4.0 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 48710
64.2%
. 25272
33.3%
1 1663
 
2.2%
2 164
 
0.2%
3 5
 
< 0.1%
4 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 50544
66.7%
Other Punctuation 25272
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 48710
96.4%
1 1663
 
3.3%
2 164
 
0.3%
3 5
 
< 0.1%
4 2
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 25272
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 75816
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 48710
64.2%
. 25272
33.3%
1 1663
 
2.2%
2 164
 
0.2%
3 5
 
< 0.1%
4 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 75816
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 48710
64.2%
. 25272
33.3%
1 1663
 
2.2%
2 164
 
0.2%
3 5
 
< 0.1%
4 2
 
< 0.1%

Накоп незачет сразу
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct61
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7934869
Minimum0
Maximum62
Zeros19155
Zeros (%)75.8%
Negative0
Negative (%)0.0%
Memory size197.6 KiB
2023-10-04T11:00:19.446798image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile12
Maximum62
Range62
Interquartile range (IQR)0

Descriptive statistics

Standard deviation5.066497
Coefficient of variation (CV)2.8249424
Kurtosis26.29078
Mean1.7934869
Median Absolute Deviation (MAD)0
Skewness4.4588713
Sum45325
Variance25.669392
MonotonicityNot monotonic
2023-10-04T11:00:19.626604image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 19155
75.8%
1 1447
 
5.7%
2 691
 
2.7%
3 442
 
1.7%
4 389
 
1.5%
7 372
 
1.5%
6 322
 
1.3%
8 304
 
1.2%
5 300
 
1.2%
9 211
 
0.8%
Other values (51) 1639
 
6.5%
ValueCountFrequency (%)
0 19155
75.8%
1 1447
 
5.7%
2 691
 
2.7%
3 442
 
1.7%
4 389
 
1.5%
5 300
 
1.2%
6 322
 
1.3%
7 372
 
1.5%
8 304
 
1.2%
9 211
 
0.8%
ValueCountFrequency (%)
62 1
 
< 0.1%
61 1
 
< 0.1%
60 2
< 0.1%
59 1
 
< 0.1%
58 1
 
< 0.1%
57 1
 
< 0.1%
55 2
< 0.1%
54 1
 
< 0.1%
53 2
< 0.1%
52 3
< 0.1%

Накоп зачет сразу
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct77
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.03308
Minimum0
Maximum81
Zeros663
Zeros (%)2.6%
Negative0
Negative (%)0.0%
Memory size197.6 KiB
2023-10-04T11:00:19.809046image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q17
median13
Q327
95-th percentile39
Maximum81
Range81
Interquartile range (IQR)20

Descriptive statistics

Standard deviation12.545983
Coefficient of variation (CV)0.73656575
Kurtosis-0.097756066
Mean17.03308
Median Absolute Deviation (MAD)9
Skewness0.75967287
Sum430460
Variance157.4017
MonotonicityNot monotonic
2023-10-04T11:00:19.972827image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 2239
 
8.9%
8 1454
 
5.8%
10 1280
 
5.1%
11 1264
 
5.0%
9 1002
 
4.0%
5 940
 
3.7%
15 760
 
3.0%
3 736
 
2.9%
6 669
 
2.6%
0 663
 
2.6%
Other values (67) 14265
56.4%
ValueCountFrequency (%)
0 663
 
2.6%
1 265
 
1.0%
2 526
 
2.1%
3 736
 
2.9%
4 2239
8.9%
5 940
3.7%
6 669
 
2.6%
7 605
 
2.4%
8 1454
5.8%
9 1002
4.0%
ValueCountFrequency (%)
81 2
< 0.1%
79 1
 
< 0.1%
78 2
< 0.1%
76 3
< 0.1%
75 2
< 0.1%
73 1
 
< 0.1%
72 1
 
< 0.1%
70 3
< 0.1%
69 3
< 0.1%
67 3
< 0.1%

Накоп удовлетворительно сразу
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct62
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4485201
Minimum0
Maximum64
Zeros9511
Zeros (%)37.6%
Negative0
Negative (%)0.0%
Memory size197.6 KiB
2023-10-04T11:00:20.143010image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q36
95-th percentile18
Maximum64
Range64
Interquartile range (IQR)6

Descriptive statistics

Standard deviation6.6051191
Coefficient of variation (CV)1.4847902
Kurtosis9.0722206
Mean4.4485201
Median Absolute Deviation (MAD)2
Skewness2.5156205
Sum112423
Variance43.627598
MonotonicityNot monotonic
2023-10-04T11:00:20.298840image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 9511
37.6%
1 2786
 
11.0%
2 2043
 
8.1%
3 1459
 
5.8%
4 1278
 
5.1%
5 1128
 
4.5%
6 864
 
3.4%
7 747
 
3.0%
8 683
 
2.7%
10 614
 
2.4%
Other values (52) 4159
16.5%
ValueCountFrequency (%)
0 9511
37.6%
1 2786
 
11.0%
2 2043
 
8.1%
3 1459
 
5.8%
4 1278
 
5.1%
5 1128
 
4.5%
6 864
 
3.4%
7 747
 
3.0%
8 683
 
2.7%
9 554
 
2.2%
ValueCountFrequency (%)
64 1
 
< 0.1%
62 1
 
< 0.1%
61 2
< 0.1%
59 2
< 0.1%
58 1
 
< 0.1%
56 1
 
< 0.1%
55 2
< 0.1%
54 1
 
< 0.1%
53 3
< 0.1%
52 2
< 0.1%

Накоп хорошо сразу
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct36
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.6502849
Minimum0
Maximum38
Zeros4179
Zeros (%)16.5%
Negative0
Negative (%)0.0%
Memory size197.6 KiB
2023-10-04T11:00:20.459467image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median4
Q39
95-th percentile16
Maximum38
Range38
Interquartile range (IQR)8

Descriptive statistics

Standard deviation5.423059
Coefficient of variation (CV)0.95978506
Kurtosis0.81406146
Mean5.6502849
Median Absolute Deviation (MAD)3
Skewness1.0954374
Sum142794
Variance29.409569
MonotonicityNot monotonic
2023-10-04T11:00:20.615260image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
0 4179
16.5%
1 2844
11.3%
2 2626
10.4%
3 2172
 
8.6%
4 1647
 
6.5%
5 1410
 
5.6%
6 1371
 
5.4%
7 1147
 
4.5%
8 1081
 
4.3%
9 1051
 
4.2%
Other values (26) 5744
22.7%
ValueCountFrequency (%)
0 4179
16.5%
1 2844
11.3%
2 2626
10.4%
3 2172
8.6%
4 1647
 
6.5%
5 1410
 
5.6%
6 1371
 
5.4%
7 1147
 
4.5%
8 1081
 
4.3%
9 1051
 
4.2%
ValueCountFrequency (%)
38 1
 
< 0.1%
36 1
 
< 0.1%
35 1
 
< 0.1%
32 2
 
< 0.1%
31 7
< 0.1%
30 4
 
< 0.1%
29 8
< 0.1%
28 10
< 0.1%
27 11
< 0.1%
26 13
0.1%

Накоп отлично сразу
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct40
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.7370212
Minimum0
Maximum42
Zeros7277
Zeros (%)28.8%
Negative0
Negative (%)0.0%
Memory size197.6 KiB
2023-10-04T11:00:20.826946image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q35
95-th percentile15
Maximum42
Range42
Interquartile range (IQR)5

Descriptive statistics

Standard deviation5.0766288
Coefficient of variation (CV)1.3584693
Kurtosis6.0629119
Mean3.7370212
Median Absolute Deviation (MAD)2
Skewness2.2737976
Sum94442
Variance25.77216
MonotonicityNot monotonic
2023-10-04T11:00:20.974777image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
0 7277
28.8%
1 3891
15.4%
2 3274
13.0%
3 2383
 
9.4%
4 1492
 
5.9%
5 1128
 
4.5%
6 1085
 
4.3%
7 739
 
2.9%
8 670
 
2.7%
9 571
 
2.3%
Other values (30) 2762
 
10.9%
ValueCountFrequency (%)
0 7277
28.8%
1 3891
15.4%
2 3274
13.0%
3 2383
 
9.4%
4 1492
 
5.9%
5 1128
 
4.5%
6 1085
 
4.3%
7 739
 
2.9%
8 670
 
2.7%
9 571
 
2.3%
ValueCountFrequency (%)
42 1
 
< 0.1%
41 1
 
< 0.1%
40 1
 
< 0.1%
39 1
 
< 0.1%
35 1
 
< 0.1%
34 1
 
< 0.1%
33 4
 
< 0.1%
32 6
 
< 0.1%
31 9
< 0.1%
30 19
0.1%

Накоп зачет с исправлением
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.040123457
Minimum0
Maximum13
Zeros24687
Zeros (%)97.7%
Negative0
Negative (%)0.0%
Memory size197.6 KiB
2023-10-04T11:00:21.102606image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum13
Range13
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.40686381
Coefficient of variation (CV)10.140298
Kurtosis505.33782
Mean0.040123457
Median Absolute Deviation (MAD)0
Skewness20.17905
Sum1014
Variance0.16553816
MonotonicityNot monotonic
2023-10-04T11:00:21.230533image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 24687
97.7%
1 442
 
1.7%
2 83
 
0.3%
3 15
 
0.1%
11 11
 
< 0.1%
5 9
 
< 0.1%
4 7
 
< 0.1%
9 4
 
< 0.1%
6 4
 
< 0.1%
10 4
 
< 0.1%
Other values (3) 6
 
< 0.1%
ValueCountFrequency (%)
0 24687
97.7%
1 442
 
1.7%
2 83
 
0.3%
3 15
 
0.1%
4 7
 
< 0.1%
5 9
 
< 0.1%
6 4
 
< 0.1%
8 2
 
< 0.1%
9 4
 
< 0.1%
10 4
 
< 0.1%
ValueCountFrequency (%)
13 3
 
< 0.1%
12 1
 
< 0.1%
11 11
< 0.1%
10 4
 
< 0.1%
9 4
 
< 0.1%
8 2
 
< 0.1%
6 4
 
< 0.1%
5 9
< 0.1%
4 7
< 0.1%
3 15
0.1%
Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1709006
Minimum0
Maximum10
Zeros22394
Zeros (%)88.6%
Negative0
Negative (%)0.0%
Memory size197.6 KiB
2023-10-04T11:00:21.350372image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum10
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.57636293
Coefficient of variation (CV)3.3725038
Kurtosis38.800277
Mean0.1709006
Median Absolute Deviation (MAD)0
Skewness5.1186847
Sum4319
Variance0.33219423
MonotonicityNot monotonic
2023-10-04T11:00:21.468613image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 22394
88.6%
1 1997
 
7.9%
2 531
 
2.1%
3 240
 
0.9%
4 61
 
0.2%
5 25
 
0.1%
7 15
 
0.1%
6 6
 
< 0.1%
10 3
 
< 0.1%
ValueCountFrequency (%)
0 22394
88.6%
1 1997
 
7.9%
2 531
 
2.1%
3 240
 
0.9%
4 61
 
0.2%
5 25
 
0.1%
6 6
 
< 0.1%
7 15
 
0.1%
10 3
 
< 0.1%
ValueCountFrequency (%)
10 3
 
< 0.1%
7 15
 
0.1%
6 6
 
< 0.1%
5 25
 
0.1%
4 61
 
0.2%
3 240
 
0.9%
2 531
 
2.1%
1 1997
 
7.9%
0 22394
88.6%

Накоп хорошо с исправлением
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.35272238
Minimum0
Maximum10
Zeros19644
Zeros (%)77.7%
Negative0
Negative (%)0.0%
Memory size197.6 KiB
2023-10-04T11:00:21.588452image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum10
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.80876624
Coefficient of variation (CV)2.2929258
Kurtosis16.714135
Mean0.35272238
Median Absolute Deviation (MAD)0
Skewness3.3490887
Sum8914
Variance0.65410283
MonotonicityNot monotonic
2023-10-04T11:00:21.760720image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 19644
77.7%
1 3577
 
14.2%
2 1296
 
5.1%
3 464
 
1.8%
4 193
 
0.8%
5 56
 
0.2%
6 23
 
0.1%
8 5
 
< 0.1%
9 5
 
< 0.1%
10 5
 
< 0.1%
ValueCountFrequency (%)
0 19644
77.7%
1 3577
 
14.2%
2 1296
 
5.1%
3 464
 
1.8%
4 193
 
0.8%
5 56
 
0.2%
6 23
 
0.1%
7 4
 
< 0.1%
8 5
 
< 0.1%
9 5
 
< 0.1%
ValueCountFrequency (%)
10 5
 
< 0.1%
9 5
 
< 0.1%
8 5
 
< 0.1%
7 4
 
< 0.1%
6 23
 
0.1%
5 56
 
0.2%
4 193
 
0.8%
3 464
 
1.8%
2 1296
 
5.1%
1 3577
14.2%
Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.41646882
Minimum0
Maximum8
Zeros18696
Zeros (%)74.0%
Negative0
Negative (%)0.0%
Memory size197.6 KiB
2023-10-04T11:00:21.927525image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.86163728
Coefficient of variation (CV)2.0689119
Kurtosis9.7927101
Mean0.41646882
Median Absolute Deviation (MAD)0
Skewness2.7746365
Sum10525
Variance0.74241881
MonotonicityNot monotonic
2023-10-04T11:00:22.092921image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 18696
74.0%
1 4141
 
16.4%
2 1475
 
5.8%
3 607
 
2.4%
4 219
 
0.9%
5 88
 
0.3%
6 29
 
0.1%
7 13
 
0.1%
8 4
 
< 0.1%
ValueCountFrequency (%)
0 18696
74.0%
1 4141
 
16.4%
2 1475
 
5.8%
3 607
 
2.4%
4 219
 
0.9%
5 88
 
0.3%
6 29
 
0.1%
7 13
 
0.1%
8 4
 
< 0.1%
ValueCountFrequency (%)
8 4
 
< 0.1%
7 13
 
0.1%
6 29
 
0.1%
5 88
 
0.3%
4 219
 
0.9%
3 607
 
2.4%
2 1475
 
5.8%
1 4141
 
16.4%
0 18696
74.0%

Накоп незачет до исправления
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.10834125
Minimum0
Maximum26
Zeros23903
Zeros (%)94.6%
Negative0
Negative (%)0.0%
Memory size197.6 KiB
2023-10-04T11:00:22.238723image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum26
Range26
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.77604518
Coefficient of variation (CV)7.1629707
Kurtosis403.48762
Mean0.10834125
Median Absolute Deviation (MAD)0
Skewness17.244335
Sum2738
Variance0.60224612
MonotonicityNot monotonic
2023-10-04T11:00:22.368622image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 23903
94.6%
1 918
 
3.6%
2 230
 
0.9%
3 65
 
0.3%
4 56
 
0.2%
5 40
 
0.2%
6 17
 
0.1%
18 8
 
< 0.1%
13 5
 
< 0.1%
12 5
 
< 0.1%
Other values (11) 25
 
0.1%
ValueCountFrequency (%)
0 23903
94.6%
1 918
 
3.6%
2 230
 
0.9%
3 65
 
0.3%
4 56
 
0.2%
5 40
 
0.2%
6 17
 
0.1%
7 2
 
< 0.1%
8 4
 
< 0.1%
9 2
 
< 0.1%
ValueCountFrequency (%)
26 3
 
< 0.1%
22 1
 
< 0.1%
20 3
 
< 0.1%
19 1
 
< 0.1%
18 8
< 0.1%
16 3
 
< 0.1%
15 3
 
< 0.1%
14 1
 
< 0.1%
13 5
< 0.1%
12 5
< 0.1%

Накоп зачет до исправления
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.38663343
Minimum0
Maximum7
Zeros18909
Zeros (%)74.8%
Negative0
Negative (%)0.0%
Memory size197.6 KiB
2023-10-04T11:00:22.562567image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum7
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.80369543
Coefficient of variation (CV)2.0787014
Kurtosis8.3073486
Mean0.38663343
Median Absolute Deviation (MAD)0
Skewness2.6517376
Sum9771
Variance0.64592635
MonotonicityNot monotonic
2023-10-04T11:00:22.738899image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 18909
74.8%
1 4209
 
16.7%
2 1284
 
5.1%
3 585
 
2.3%
4 202
 
0.8%
5 71
 
0.3%
6 8
 
< 0.1%
7 4
 
< 0.1%
ValueCountFrequency (%)
0 18909
74.8%
1 4209
 
16.7%
2 1284
 
5.1%
3 585
 
2.3%
4 202
 
0.8%
5 71
 
0.3%
6 8
 
< 0.1%
7 4
 
< 0.1%
ValueCountFrequency (%)
7 4
 
< 0.1%
6 8
 
< 0.1%
5 71
 
0.3%
4 202
 
0.8%
3 585
 
2.3%
2 1284
 
5.1%
1 4209
 
16.7%
0 18909
74.8%
Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.23480532
Minimum0
Maximum10
Zeros21576
Zeros (%)85.4%
Negative0
Negative (%)0.0%
Memory size197.6 KiB
2023-10-04T11:00:22.871123image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum10
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.70450439
Coefficient of variation (CV)3.0003766
Kurtosis31.122041
Mean0.23480532
Median Absolute Deviation (MAD)0
Skewness4.6325423
Sum5934
Variance0.49632643
MonotonicityNot monotonic
2023-10-04T11:00:23.003219image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 21576
85.4%
1 2390
 
9.5%
2 808
 
3.2%
3 266
 
1.1%
4 127
 
0.5%
5 56
 
0.2%
6 31
 
0.1%
8 5
 
< 0.1%
9 5
 
< 0.1%
10 5
 
< 0.1%
ValueCountFrequency (%)
0 21576
85.4%
1 2390
 
9.5%
2 808
 
3.2%
3 266
 
1.1%
4 127
 
0.5%
5 56
 
0.2%
6 31
 
0.1%
7 3
 
< 0.1%
8 5
 
< 0.1%
9 5
 
< 0.1%
ValueCountFrequency (%)
10 5
 
< 0.1%
9 5
 
< 0.1%
8 5
 
< 0.1%
7 3
 
< 0.1%
6 31
 
0.1%
5 56
 
0.2%
4 127
 
0.5%
3 266
 
1.1%
2 808
 
3.2%
1 2390
9.5%

отчислен
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size197.6 KiB
0
16824 
1
8448 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters25272
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 16824
66.6%
1 8448
33.4%

Length

2023-10-04T11:00:23.163114image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-04T11:00:23.294974image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0 16824
66.6%
1 8448
33.4%

Most occurring characters

ValueCountFrequency (%)
0 16824
66.6%
1 8448
33.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 25272
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16824
66.6%
1 8448
33.4%

Most occurring scripts

ValueCountFrequency (%)
Common 25272
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16824
66.6%
1 8448
33.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 25272
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16824
66.6%
1 8448
33.4%

Interactions

2023-10-04T11:00:09.662729image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:20.746971image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:22.981313image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:25.160854image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:27.306075image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:29.556043image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:31.738608image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:34.016635image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:36.328792image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:38.636288image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:40.976446image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:43.655013image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:46.680658image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:49.820260image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:53.336823image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:56.698399image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:59.776458image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T11:00:03.295623image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T11:00:06.378608image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T11:00:09.804577image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:20.868899image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:23.093524image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:25.270965image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:27.417933image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:29.668899image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:31.847543image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:34.137771image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:36.440628image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:38.748446image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:41.113119image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:43.778946image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:46.860251image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:49.967733image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:53.484588image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
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2023-10-04T10:59:30.929124image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:33.222658image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:35.452450image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:37.710722image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:40.052522image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:42.641847image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:45.364523image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:48.690289image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:51.795655image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:55.373037image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:58.598446image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T11:00:01.981156image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T11:00:05.242324image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T11:00:08.566633image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T11:00:11.751876image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:22.301020image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:24.468950image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:26.625278image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:28.875477image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:31.048966image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:33.326517image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:35.598701image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:37.828597image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:40.176827image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:42.773942image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:45.595927image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:48.838057image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:52.124208image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:55.586812image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:58.812670image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T11:00:02.263053image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T11:00:05.390434image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T11:00:08.726527image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T11:00:11.912866image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:22.414902image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:24.580803image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:26.737532image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:28.987370image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:31.160845image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:33.443661image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:35.710589image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:37.942333image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:40.313211image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:42.913238image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:45.872005image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:49.002015image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:52.308467image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:55.814542image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:58.974530image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T11:00:02.470678image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T11:00:05.570166image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T11:00:08.883462image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T11:00:12.092670image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:22.526496image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:24.688702image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:26.853503image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:29.095682image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:31.272665image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:33.557965image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:35.828997image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:38.060148image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:40.432639image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:43.042392image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:46.022955image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:49.157586image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:52.464768image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:55.982770image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:59.141266image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T11:00:02.614847image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T11:00:05.719110image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T11:00:09.026724image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T11:00:12.268629image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:22.641224image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:24.816971image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:26.969491image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:29.219552image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:31.392534image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:33.677575image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:35.956780image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:38.184191image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:40.571002image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:43.214756image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:46.200409image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:49.338509image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:52.636446image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:56.136876image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:59.313488image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T11:00:02.771159image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T11:00:05.874995image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T11:00:09.180601image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T11:00:12.456679image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:22.753236image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:24.933102image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:27.078235image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:29.324439image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:31.508349image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:33.785795image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:36.076686image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:38.296438image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:40.692690image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:43.354679image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:46.364904image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:49.478189image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:52.792816image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:56.298936image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:59.477797image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T11:00:02.935657image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T11:00:06.023215image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T11:00:09.334547image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T11:00:12.646696image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:22.869271image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:25.044968image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:27.194143image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:29.446769image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:31.628220image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:33.905703image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:36.194237image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:38.518691image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:40.842522image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:43.498856image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:46.520801image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:49.634128image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:53.192398image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:56.463144image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T10:59:59.637829image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T11:00:03.102513image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T11:00:06.224789image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-04T11:00:09.514755image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Correlations

2023-10-04T11:00:23.580887image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Семестрзачет сразуудовлетворительно сразухорошо сразуотлично сразузачет с исправлениемнезачет до исправленияНакоп незачет сразуНакоп зачет сразуНакоп удовлетворительно сразуНакоп хорошо сразуНакоп отлично сразуНакоп зачет с исправлениемНакоп удовлетворительно с исправлениемНакоп хорошо с исправлениемНакоп отлично с исправлениемНакоп незачет до исправленияНакоп зачет до исправленияНакоп удовлетворительно до исправленияФорма обученияКвалификациянезачет сразуудовлетворительно с исправлениемхорошо с исправлениемотлично с исправлениемзачет до исправленияудовлетворительно до исправленияхорошо до исправленияотчислен
Семестр1.000-0.0320.0300.002-0.013-0.024-0.0110.0520.8870.4970.6980.4480.0770.3210.4760.4080.1380.4920.4010.2090.2480.1030.0670.0970.0900.1460.1110.0700.110
зачет сразу-0.0321.0000.0900.3200.279-0.062-0.070-0.4310.266-0.0220.2040.213-0.112-0.080-0.0000.081-0.1420.054-0.0040.2780.2650.5170.0420.0500.0840.1010.0480.0740.397
удовлетворительно сразу0.0300.0901.000-0.039-0.410-0.007-0.001-0.0200.0980.6280.057-0.3560.0170.1690.085-0.1640.0190.0150.1440.1870.1790.0850.0630.0510.0720.0120.0750.0560.127
хорошо сразу0.0020.320-0.0391.000-0.108-0.032-0.045-0.2670.1270.0470.475-0.016-0.055-0.0120.0610.023-0.0750.0420.0160.0890.1060.2210.0000.0200.0360.0470.0420.0280.152
отлично сразу-0.0130.279-0.410-0.1081.000-0.020-0.047-0.3350.093-0.425-0.0370.681-0.070-0.166-0.1130.177-0.1010.053-0.1580.2430.1930.2380.0350.0430.0580.1020.0680.0460.308
зачет с исправлением-0.024-0.062-0.007-0.032-0.0201.0000.5810.058-0.048-0.010-0.037-0.0300.4740.0690.025-0.0020.309-0.009-0.0140.0280.0480.0660.2920.2700.0000.0000.0000.0000.060
незачет до исправления-0.011-0.070-0.001-0.045-0.0470.5811.0000.115-0.0410.009-0.033-0.0470.3250.1550.0660.0110.526-0.017-0.0080.0460.0500.1190.3740.3430.0110.0000.0000.0000.105
Накоп незачет сразу0.052-0.431-0.020-0.267-0.3350.0580.1151.000-0.1130.160-0.112-0.2920.1740.1170.009-0.1140.245-0.036-0.0030.1870.0770.6250.0000.0170.0430.0380.0200.0320.470
Накоп зачет сразу0.8870.2660.0980.1270.093-0.048-0.041-0.1131.0000.5190.7730.5100.0200.2780.4480.4230.0730.4920.3860.1110.2610.2490.0750.1050.0880.1680.1100.0680.217
Накоп удовлетворительно сразу0.497-0.0220.6280.047-0.425-0.0100.0090.1600.5191.0000.427-0.2180.0880.3780.3520.0080.1310.2640.3560.2140.1640.0340.0790.0630.0330.0520.0760.0240.174
Накоп хорошо сразу0.6980.2040.0570.475-0.037-0.037-0.033-0.1120.7730.4271.0000.3340.0160.2380.4240.3290.0600.4030.3300.0700.2440.2060.0470.0930.0610.1050.0840.0520.145
Накоп отлично сразу0.4480.213-0.356-0.0160.681-0.030-0.047-0.2920.510-0.2180.3341.000-0.041-0.0110.1400.421-0.0380.3120.0540.1550.1280.2160.0190.0240.0960.0830.0240.0560.256
Накоп зачет с исправлением0.077-0.1120.017-0.055-0.0700.4740.3250.1740.0200.0880.016-0.0411.0000.1780.1080.0060.6520.0530.0440.0480.0770.0830.3280.2890.0000.0000.0000.0000.087
Накоп удовлетворительно с исправлением0.321-0.0800.169-0.012-0.1660.0690.1550.1170.2780.3780.238-0.0110.1781.0000.2190.0270.3370.5200.2200.1120.0610.0640.2820.0480.0000.1070.0460.0000.099
Накоп хорошо с исправлением0.476-0.0000.0850.061-0.1130.0250.0660.0090.4480.3520.4240.1400.1080.2191.0000.2380.1880.4860.7240.0850.0770.0510.0190.2340.0170.0640.2090.0050.043
Накоп отлично с исправлением0.4080.081-0.1640.0230.177-0.0020.011-0.1140.4230.0080.3290.4210.0060.0270.2381.0000.0630.3920.2430.0780.0860.1590.0170.0340.3670.1290.0730.2950.181
Накоп незачет до исправления0.138-0.1420.019-0.075-0.1010.3090.5260.2450.0730.1310.060-0.0380.6520.3370.1880.0631.0000.0330.1110.0590.0760.1120.3760.2160.0000.0000.0000.0000.111
Накоп зачет до исправления0.4920.0540.0150.0420.053-0.009-0.017-0.0360.4920.2640.4030.3120.0530.5200.4860.3920.0331.0000.1990.0860.1470.1140.1500.1500.1300.3740.0610.0290.105
Накоп удовлетворительно до исправления0.401-0.0040.1440.016-0.158-0.014-0.008-0.0030.3860.3560.3300.0540.0440.2200.7240.2430.1110.1991.0000.1000.0660.0550.0000.1440.0360.0000.2520.0050.058
Форма обучения0.2090.2780.1870.0890.2430.0280.0460.1870.1110.2140.0700.1550.0480.1120.0850.0780.0590.0860.1001.0000.2950.2480.0590.0270.0900.0340.0550.0750.302
Квалификация0.2480.2650.1790.1060.1930.0480.0500.0770.2610.1640.2440.1280.0770.0610.0770.0860.0760.1470.0660.2951.0000.1120.0460.0540.0960.0890.0470.0800.206
незачет сразу0.1030.5170.0850.2210.2380.0660.1190.6250.2490.0340.2060.2160.0830.0640.0510.1590.1120.1140.0550.2480.1121.0000.0230.0720.1220.1000.0670.1000.634
удовлетворительно с исправлением0.0670.0420.0630.0000.0350.2920.3740.0000.0750.0790.0470.0190.3280.2820.0190.0170.3760.1500.0000.0590.0460.0231.0000.0000.0210.3100.0000.0180.036
хорошо с исправлением0.0970.0500.0510.0200.0430.2700.3430.0170.1050.0630.0930.0240.2890.0480.2340.0340.2160.1500.1440.0270.0540.0720.0001.0000.0110.3210.6280.0000.049
отлично с исправлением0.0900.0840.0720.0360.0580.0000.0110.0430.0880.0330.0610.0960.0000.0000.0170.3670.0000.1300.0360.0900.0960.1220.0210.0111.0000.2600.0610.7760.144
зачет до исправления0.1460.1010.0120.0470.1020.0000.0000.0380.1680.0520.1050.0830.0000.1070.0640.1290.0000.3740.0000.0340.0890.1000.3100.3210.2601.0000.0130.0200.080
удовлетворительно до исправления0.1110.0480.0750.0420.0680.0000.0000.0200.1100.0760.0840.0240.0000.0460.2090.0730.0000.0610.2520.0550.0470.0670.0000.6280.0610.0131.0000.0000.054
хорошо до исправления0.0700.0740.0560.0280.0460.0000.0000.0320.0680.0240.0520.0560.0000.0000.0050.2950.0000.0290.0050.0750.0800.1000.0180.0000.7760.0200.0001.0000.116
отчислен0.1100.3970.1270.1520.3080.0600.1050.4700.2170.1740.1450.2560.0870.0990.0430.1810.1110.1050.0580.3020.2060.6340.0360.0490.1440.0800.0540.1161.000

Missing values

2023-10-04T11:00:12.926579image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
A simple visualization of nullity by column.
2023-10-04T11:00:13.596367image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

СеместрФорма обученияКвалификациянезачет сразузачет сразуудовлетворительно сразухорошо сразуотлично сразузачет с исправлениемудовлетворительно с исправлениемхорошо с исправлениемотлично с исправлениемнезачет до исправлениязачет до исправленияудовлетворительно до исправленияхорошо до исправленияНакоп незачет сразуНакоп зачет сразуНакоп удовлетворительно сразуНакоп хорошо сразуНакоп отлично сразуНакоп зачет с исправлениемНакоп удовлетворительно с исправлениемНакоп хорошо с исправлениемНакоп отлично с исправлениемНакоп незачет до исправленияНакоп зачет до исправленияНакоп удовлетворительно до исправленияотчислен
012012.00.01.00.00.00.00.00.00.00.00.00.04.02.00.01.00.00.00.00.00.00.00.00.01
122010.00.00.00.00.00.00.00.00.00.00.00.012.02.00.01.00.00.00.00.00.00.00.00.01
232010.00.00.00.00.00.00.00.00.00.00.00.017.02.00.01.00.00.00.00.00.00.00.00.01
342010.00.00.00.00.00.00.00.00.00.00.00.023.02.00.01.00.00.00.00.00.00.00.00.01
412006.00.01.01.00.00.00.00.00.00.00.00.00.06.00.01.01.00.00.00.00.00.00.00.00
522005.01.00.02.00.00.01.00.00.00.01.00.00.011.01.01.03.00.00.01.00.00.00.01.00
632002.00.02.01.00.00.01.00.00.00.01.00.00.013.01.03.04.00.00.02.00.00.00.02.00
742004.00.00.02.00.00.00.01.00.00.01.00.00.017.01.03.06.00.00.02.01.00.00.03.00
852005.00.02.00.00.00.01.00.00.00.01.00.00.022.01.05.06.00.00.03.01.00.00.04.00
962004.00.02.00.00.00.01.00.00.00.01.00.00.026.01.07.06.00.00.04.01.00.00.05.00
СеместрФорма обученияКвалификациянезачет сразузачет сразуудовлетворительно сразухорошо сразуотлично сразузачет с исправлениемудовлетворительно с исправлениемхорошо с исправлениемотлично с исправлениемнезачет до исправлениязачет до исправленияудовлетворительно до исправленияхорошо до исправленияНакоп незачет сразуНакоп зачет сразуНакоп удовлетворительно сразуНакоп хорошо сразуНакоп отлично сразуНакоп зачет с исправлениемНакоп удовлетворительно с исправлениемНакоп хорошо с исправлениемНакоп отлично с исправлениемНакоп незачет до исправленияНакоп зачет до исправленияНакоп удовлетворительно до исправленияотчислен
2526232014.00.02.00.00.00.00.00.00.00.00.00.03.021.05.013.00.00.00.00.00.00.00.00.01
2526332011.01.00.00.00.00.00.00.00.00.00.00.08.022.06.013.00.00.00.00.00.00.00.00.01
2526432003.02.01.00.00.00.00.00.00.00.00.00.08.025.08.014.00.00.00.00.00.00.00.00.01
2526542003.01.03.00.00.00.00.00.00.00.00.00.08.028.09.017.00.00.00.00.00.00.00.00.01
2526652006.00.01.01.00.00.00.00.00.00.00.00.08.034.09.018.01.00.00.00.00.00.00.00.01
2526762006.00.02.00.00.00.00.00.00.00.00.00.08.040.09.020.01.00.00.00.00.00.00.00.01
2526872004.00.02.01.00.00.00.00.00.00.00.00.08.044.09.022.02.00.00.00.00.00.00.00.01
2526982005.00.01.01.00.00.00.01.00.00.00.01.08.049.09.023.03.00.00.00.01.00.00.00.01
2527092002.01.02.00.00.00.00.01.00.00.00.01.08.051.010.025.03.00.00.00.02.00.00.00.01
25271102001.00.02.00.00.00.00.00.00.00.00.00.08.052.010.027.03.00.00.00.02.00.00.00.01

Duplicate rows

Most frequently occurring

СеместрФорма обученияКвалификациянезачет сразузачет сразуудовлетворительно сразухорошо сразуотлично сразузачет с исправлениемудовлетворительно с исправлениемхорошо с исправлениемотлично с исправлениемнезачет до исправлениязачет до исправленияудовлетворительно до исправленияхорошо до исправленияНакоп незачет сразуНакоп зачет сразуНакоп удовлетворительно сразуНакоп хорошо сразуНакоп отлично сразуНакоп зачет с исправлениемНакоп удовлетворительно с исправлениемНакоп хорошо с исправлениемНакоп отлично с исправлениемНакоп незачет до исправленияНакоп зачет до исправленияНакоп удовлетворительно до исправленияотчислен# duplicates
21510204.00.00.03.00.00.00.00.00.00.00.00.00.04.00.00.03.00.00.00.00.00.00.00.00232
28410302.00.00.00.00.00.00.00.00.00.00.00.00.02.00.00.00.00.00.00.00.00.00.00.00171
71320204.00.00.03.00.00.00.00.00.00.00.00.00.08.00.00.06.00.00.00.00.00.00.00.00128
22210204.00.01.02.00.00.00.00.00.00.00.00.00.04.00.01.02.00.00.00.00.00.00.00.00118
82620302.00.00.02.00.00.00.00.00.00.00.00.00.04.00.00.02.00.00.00.00.00.00.00.0097
123230304.00.00.00.00.00.00.00.00.00.00.00.00.08.00.00.02.00.00.00.00.00.00.00.0094
136540302.00.00.01.00.00.00.00.00.00.00.00.00.010.00.00.03.00.00.00.00.00.00.00.0081
39212010.00.00.00.00.00.00.00.00.00.00.00.07.00.00.00.00.00.00.00.00.00.00.00.0178
23110204.00.02.01.00.00.00.00.00.00.00.00.00.04.00.02.01.00.00.00.00.00.00.00.0073
1910004.00.03.01.00.00.00.00.00.00.00.00.00.04.00.03.01.00.00.00.00.00.00.00.0068